CN116956036A - Crop yield estimation method based on multi-source data - Google Patents
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Abstract
The invention discloses a crop yield estimation method based on multi-source data, which relates to the technical field of accurate agriculture and agriculture informatization, and comprises the following steps of firstly, obtaining sample data of a target area, processing the sample data to obtain an initial index value of the estimated yield of the target area, secondly, obtaining multi-source information of a decomposed crop model, and a key growth functional module controlled by parameters of the crop model comprises the following steps: the multi-source information comprises basic input information, remote sensing correction information and yield correction information. The invention uses remote sensing data and meteorological data, combines a leave-one-process modeling strategy and a random forest variable importance evaluation method, selects high-precision and high-stability input variables in a partitioned and hierarchical manner, and constructs a unit yield estimation model of various crops in a target area based on a random forest regression algorithm so as to accurately predict the unit yield of the crops under different agricultural climate conditions.
Description
Technical Field
The invention relates to the technical field of precise agriculture and agricultural informatization, in particular to a crop yield estimation method based on multi-source data.
Background
Crops are taken as important economic crops and international trade agricultural products, the industrial agricultural economic support industry in vast areas in south China is becoming one of the important economic crops and international trade agricultural products, along with research and application of agricultural information technology, traditional agriculture is gradually transformed into information agriculture and technological agriculture, the remote sensing technology plays an increasingly important role in the technological agriculture transformation process due to the characteristic of large-area synchronous observation, and is different from the traditional crop estimation method, the remote sensing technology provides a new scientific and technological means for comprehensive, macroscopic, rapid and dynamic observation of crops, currently, real-time remote sensing information is combined with a crop growth mechanism model to realize dynamic monitoring of regional crop growth, especially yield estimation, the application of the remote sensing information in accurate agricultural estimation in China is promoted, the predicted yield is the important content of fine management of crop production in China, most of domestic scholars currently use single-phase and multi-time spectral estimation models and a composite estimation model combined with crop growth to estimate the rice yield by using cap layer high-level data of mature period rice, and Tang Yanlin and the like; lei Tong et al actually measure the canopy reflectance spectrum of the apple in the perchia city, and establish a regression model through the ratio index of the sensitive wave band and the fruit tree, thereby realizing the nondestructive estimation of the canopy fruit quantity of the apple; li Mingxia, european Wen Hao et al model the crop by hyperspectral remote sensing technology by comparing the difference of spectral characteristics between the crops and the critical period of yield formation, and select the optimal time phase for estimating yield; extracting main agronomic parameters of tomatoes by scientific research and the like, establishing a composite spectrum estimated yield model based on the agronomic parameters, and estimating the yield of the tomatoes in 4 growth periods; huang Jingfeng et al use the ground spectrum data and agronomic parameter data of winter wheat to build a remote sensing estimation model of winter wheat development period.
The following problems exist in the prior art:
when the crop estimation method based on the multi-source data is used, the existing crop estimation method based on the multi-source data can not be used for prediction according to different weather conditions; when the crop estimation method based on the multi-source data is used, the existing crop estimation method based on the multi-source data is not stable enough and is not beneficial to popularization; when the crop yield estimation method based on the multi-source data is used, the existing crop yield estimation method based on the multi-source data cannot estimate the yield well.
Disclosure of Invention
The invention provides a crop yield estimation method based on multi-source data, which aims to solve the problems in the background technology.
In order to solve the technical problems, the invention adopts the following technical scheme:
a crop yield estimation method based on multi-source data comprises the following steps,
step one, acquiring sample data of a target area, and processing the sample data to obtain an initial index value of the estimated yield of the target area.
Step two, multi-source information of the decomposed crop model is obtained, and the key growth function module controlled by crop model parameters comprises: the multi-source information comprises basic input information, remote sensing correction information and yield correction information.
Step three, correcting the crop model step by step; the correction sequence is a correction growth period module, a LAI module and a yield module, the optimization algorithm is an important method for improving model parameters, and the basic idea is to re-initialize crop model parameters by adopting an evolution formula through repeated iteration of differences between simulation values and observed values so as to achieve the aim of model optimization.
And step four, acquiring historical data, wherein the historical data comprise enhanced vegetation indexes of different years and different periods, statistical yields of different years of grain crops and trend yields of different years of grain crops.
The technical scheme of the invention is further improved as follows: the first step is to conduct zoning operation on the target area according to the agricultural meteorological index value to obtain a plurality of estimated production areas; and in each estimated yield area, a random forest importance evaluation algorithm is adopted to carry out importance ranking on estimated yield initial index values of each crop and carry out primary screening operation, so as to obtain estimated yield index values after the primary screening operation.
The technical scheme of the invention is further improved as follows: and step one, calculating the precision of each unit yield estimation model in the initial unit yield estimation model set corresponding to each crop in each estimated yield area, and determining the unit yield estimation model with the highest model precision in the initial unit yield estimation model set as the final unit yield estimation model of the marked crop in the marked estimated yield area.
The technical scheme of the invention is further improved as follows: and step two, the growth period module determines the nutrition growth period date and the reproductive growth period date of crops, the LAI growth functions of leaves are different in different growth and development stages, and the biomass accumulation process of the crops is concentrated in the reproductive growth period stage and is influenced by the respiration, photosynthesis and transpiration processes of the leaves.
The technical scheme of the invention is further improved as follows: and secondly, basic input information comprises daily value meteorological data, soil attribute information and initial parameters to be calibrated for driving the crop model to operate, remote sensing correction information is required to simultaneously meet the requirements of reflecting the growth and development changes of crops and monitoring the growth conditions of the crops in a large range, yield correction information is required, and ground yield observation data are used for correcting the simulated yield of the crop model.
The technical scheme of the invention is further improved as follows: and step four, calculating the pitch of the enhanced vegetation indexes according to the enhanced vegetation indexes in different years and different periods, taking the pitch of the enhanced vegetation indexes in the ith period as an independent variable, taking the estimated yield difference value as a dependent variable, and constructing a linear regression equation.
The technical scheme of the invention is further improved as follows: and step four, calculating the error of the estimated production model in the ith period to obtain the estimated production value in the ith period.
By adopting the technical scheme, compared with the prior art, the invention has the following technical progress:
1. the invention provides a crop yield estimation method based on multi-source data, which uses remote sensing data and meteorological data, combines a leave-one-process modeling strategy and a random forest variable importance evaluation method, selects high-precision and high-stability input variables in a partitioned and hierarchical manner, constructs a unit yield estimation model of multiple crops in a target area based on a random forest regression algorithm, can accurately predict the unit yield of the crops under different agricultural climate conditions, and can provide a method reference for carrying out yield estimation modeling on fine screening variables.
2. The invention provides a crop yield estimation method based on multi-source data, which corrects crop models step by step according to the logical sequence of the growth and development of crops, realizes high-quality simulation of the growth and development process of the crops, thereby ensuring stable and reliable final yield estimation.
3. The invention provides a crop yield estimation method based on multi-source data, which takes trend yield as a base number, calculates yield fluctuation value caused by short-term environmental element change at regular intervals, estimates crop yield, adopts enhanced vegetation index as an environmental influence factor, and can comprehensively reflect changes of meteorological factors, soil factors and field management factors, so that the grain crop yield estimation method and system provided by the invention can realize accurate, effective and high-instantaneity grain crop yield estimation.
Drawings
FIG. 1 is a schematic diagram of the process flow structure of the present invention.
Detailed Description
The invention is further illustrated by the following examples:
example 1
As shown in fig. 1, the present invention provides a crop estimation method based on multi-source data, which includes the following steps, step one, obtaining sample data of a target area, and processing the sample data to obtain an initial index value of estimation of the target area, step two, obtaining multi-source information of a decomposed crop model, and a key growth function module controlled by parameters of the crop model includes: the multi-source information comprises basic input information, remote sensing correction information and yield correction information, and the crop model is corrected step by step; the correction sequence is a correction growth period module, a LAI module and a yield module, the optimization algorithm is an important method for improving model parameters, the basic idea is that the evolution formula is adopted to reinitialize crop model parameters through the difference between repeated iteration simulation values and observation values, the step four is to obtain historical data, and the historical data comprises enhanced vegetation indexes of different years and different periods, statistical yields of different years of grain crops and trend yields of different years of grain crops.
In the embodiment, remote sensing data and meteorological data are used, a modeling strategy of a leave-one method and an importance evaluation method of random forest variables are combined, input variables with high precision and high stability are selected in a layered manner, a single-yield estimation model of various crops in a target area is built based on a random forest regression algorithm, so that single-yield of the crops under different agricultural climate conditions can be accurately predicted, and a method reference can be provided for estimating and modeling the screened variables.
Example 2
As shown in fig. 1, on the basis of embodiment 1, the present invention provides a technical solution: preferably, the first step is to perform a zoning operation on the target area according to the agricultural meteorological index value to obtain a plurality of estimated production areas; in each estimated yield area, a random forest importance evaluation algorithm is adopted, importance ranking is carried out on estimated yield initial index values of each crop, primary screening operation is carried out, estimated yield index values after the primary screening operation are obtained, firstly, the precision of each unit yield estimation model in an initial unit yield estimation model set corresponding to each crop in each estimated yield area is calculated, and a unit yield estimation model with highest model precision in the initial unit yield estimation model set is determined as a final unit yield estimation model of the marked crops in the marked estimated yield area.
In the embodiment, remote sensing data and meteorological data are used, a leave-one-process modeling strategy and a random forest variable importance evaluation method are combined, input variables with high precision and high stability are selected in a partitioned and hierarchical mode, a unit yield estimation model of multiple crops in a target area is built based on a random forest regression algorithm, unit yields of crops under different agricultural climate conditions can be accurately predicted, and a method reference can be provided for fine screening variables to carry out unit yield estimation modeling.
Example 3
As shown in fig. 1, on the basis of embodiment 1, the present invention provides a technical solution: preferably, the second step, the growth period module determines the date of the vegetative growth period and the date of the reproductive growth period of the crop, the LAI growth functions of the leaves are different in different growth and development stages, the biomass accumulation process of the crop is concentrated in the reproductive growth period and is influenced by the respiration, photosynthesis and transpiration processes of the leaves, the second step, the basic input information comprises daily meteorological data, soil attribute information and initial parameters to be calibrated for driving the crop model to operate, the remote sensing correction information needs to simultaneously meet the requirements for reflecting the growth and development changes of the crop and monitoring the growth conditions of the crop in a large range, the yield correction information and the ground yield observation data are used for correcting the simulation yield of the crop model.
In the embodiment, the method corrects the crop model step by step according to the logical sequence of the growth and development of the crop, realizes high-quality simulation of the growth and development process of the crop, thereby ensuring stable and reliable final yield estimation.
Example 4
As shown in fig. 1, on the basis of embodiment 1, the present invention provides a technical solution: preferably, the fourth step is to calculate the distance level of the enhanced vegetation index according to the enhanced vegetation index in different periods of different years, take the distance level of the enhanced vegetation index in the ith period as an independent variable, take the estimated yield difference value as a dependent variable, and construct a linear regression equation, and the fourth step is to calculate the error of the estimated yield model in the ith period to obtain the estimated yield value in the ith period.
In the embodiment, the method and the system for estimating the yield of the grain crops in real time take the trend yield as the base number, periodically calculate the yield fluctuation value caused by the change of the short-term environmental factors, estimate the yield of the crops, and adopt the enhanced vegetation index as the environmental influence factor to comprehensively reflect the changes of the meteorological factors, the soil factors and the field management factors, so that the method and the system for estimating the yield of the grain crops in real time can realize accurate, effective and high-real-time grain crop estimation.
The working principle of the crop estimation method based on the multi-source data is specifically described below.
As shown in fig. 1, step one, obtaining sample data of a target area, processing the sample data to obtain an initial index value of estimated production of the target area, and performing partitioning operation on the target area according to the agricultural meteorological index value to obtain a plurality of estimated production areas; in each estimated yield area, a random forest importance evaluation algorithm is adopted, importance ranking is carried out on estimated yield initial index values of each crop, primary screening operation is carried out, estimated yield index values after the primary screening operation are obtained, the precision of each unit yield estimation model in the initial unit yield estimation model set corresponding to each crop in each estimated yield area is calculated, a unit yield estimation model with highest model precision in the initial unit yield estimation model set is determined as a final unit yield estimation model of the marked crop in the marked estimated yield area, step two, multi-source information of a decomposed crop model is obtained, and a key growth function module controlled by crop model parameters comprises: the growth period module determines the nutrition growth period date and the reproductive growth period date of crops, the LAI growth functions of blades are different in different growth and development stages, the biomass accumulation process of the crops is concentrated in the reproductive growth period stage and is influenced by the respiration, photosynthesis and transpiration processes of the blades, the multi-source information comprises basic input information, remote sensing correction information and yield correction information, the basic input information comprises daily value meteorological data for driving a crop model to operate, soil attribute information and initial parameters to be calibrated, the remote sensing correction information is required to simultaneously meet the requirements for reflecting the growth and development changes of the crops and monitoring the growth conditions of the crops in a large range, the yield correction information and ground yield observation data are used for correcting the simulation yield of the crop model, and the crop model is corrected step three in steps; the correction sequence is a correction growth period module, a LAI module and a yield module, the optimization algorithm is an important method for improving model parameters, the basic idea is that an evolution formula is adopted, the crop model parameters are reinitialized through a plurality of iteration differences between analog values and observed values, step four, historical data are obtained, the historical data comprise enhanced vegetation indexes of different years and different periods, statistical yields of different years of grain crops and trend yields of different years of grain crops, the pitch of the enhanced vegetation indexes is calculated according to the enhanced vegetation indexes of different years and different periods, the pitch of the enhanced vegetation indexes of the ith period is taken as an independent variable, an estimated yield difference value is taken as a dependent variable, a linear regression equation is constructed, the error of the estimated yield model of the ith period is calculated, and the estimated yield value of the ith period is obtained.
The foregoing invention has been generally described in great detail, but it will be apparent to those skilled in the art that modifications and improvements can be made thereto. Accordingly, it is intended to cover modifications or improvements within the spirit of the inventive concepts.
Claims (7)
1. A crop estimation method based on multi-source data, which is characterized in that: comprises the steps of,
step one, acquiring sample data of a target area, and processing the sample data to obtain an initial index value of the estimated production of the target area;
step two, multi-source information of the decomposed crop model is obtained, and the key growth function module controlled by crop model parameters comprises: the system comprises a growth period module, an LAI module and a yield module, wherein the multi-source information comprises basic input information, remote sensing correction information and yield correction information;
step three, correcting the crop model step by step; the correction sequence is a correction growth period module, a LAI module and a yield module, the optimization algorithm is an important method for improving model parameters, and the basic idea is that the model parameters are reinitialized by adopting an evolution formula through repeated iteration of the difference between the simulation value and the observed value so as to achieve the aim of model optimization;
and step four, acquiring historical data, wherein the historical data comprise enhanced vegetation indexes of different years and different periods, statistical yields of different years of grain crops and trend yields of different years of grain crops.
2. A method of estimating crop yield based on multi-source data according to claim 1, wherein: the first step is to conduct zoning operation on the target area according to the agricultural meteorological index value to obtain a plurality of estimated production areas; and in each estimated yield area, a random forest importance evaluation algorithm is adopted to carry out importance ranking on estimated yield initial index values of each crop and carry out primary screening operation, so as to obtain estimated yield index values after the primary screening operation.
3. A method of estimating crop yield based on multi-source data according to claim 2, wherein: and step one, calculating the precision of each unit yield estimation model in the initial unit yield estimation model set corresponding to each crop in each estimated yield area, and determining the unit yield estimation model with the highest model precision in the initial unit yield estimation model set as the final unit yield estimation model of the marked crop in the marked estimated yield area.
4. A method of estimating crop yield based on multi-source data according to claim 1, wherein: and step two, the growth period module determines the nutrition growth period date and the reproductive growth period date of crops, the LAI growth functions of leaves are different in different growth and development stages, and the biomass accumulation process of the crops is concentrated in the reproductive growth period stage and is influenced by the respiration, photosynthesis and transpiration processes of the leaves.
5. A method of estimating crop yield based on multi-source data according to claim 1, wherein: and secondly, basic input information comprises daily value meteorological data, soil attribute information and initial parameters to be calibrated for driving the crop model to operate, remote sensing correction information is required to simultaneously meet the requirements of reflecting the growth and development changes of crops and monitoring the growth conditions of the crops in a large range, yield correction information is required, and ground yield observation data are used for correcting the simulated yield of the crop model.
6. A method of estimating crop yield based on multi-source data according to claim 1, wherein: and step four, calculating the pitch of the enhanced vegetation indexes according to the enhanced vegetation indexes in different years and different periods, taking the pitch of the enhanced vegetation indexes in the ith period as an independent variable, taking the estimated yield difference value as a dependent variable, and constructing a linear regression equation.
7. The method of multi-source data based crop estimation of claim 6, wherein: and step four, calculating the error of the estimated production model in the ith period to obtain the estimated production value in the ith period.
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| CN117744898A (en) * | 2024-02-21 | 2024-03-22 | 上海兰桂骐技术发展股份有限公司 | Construction method of annual prediction model of yield of field grain crops |
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| CN115660166A (en) * | 2022-10-24 | 2023-01-31 | 北京师范大学 | Method and device for estimating yield of multiple crops, electronic equipment and storage medium |
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| CN109359862A (en) * | 2018-10-17 | 2019-02-19 | 北京师范大学 | A kind of real-time yield estimation method of cereal crops and system |
| CN109614763A (en) * | 2019-01-30 | 2019-04-12 | 北京师范大学 | A kind of area crops yield estimation method correcting crop modeling based on multi-source information substep |
| CN115660166A (en) * | 2022-10-24 | 2023-01-31 | 北京师范大学 | Method and device for estimating yield of multiple crops, electronic equipment and storage medium |
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| CN117744898A (en) * | 2024-02-21 | 2024-03-22 | 上海兰桂骐技术发展股份有限公司 | Construction method of annual prediction model of yield of field grain crops |
| CN117744898B (en) * | 2024-02-21 | 2024-05-28 | 上海兰桂骐技术发展股份有限公司 | Construction method of annual prediction model of yield of field grain crops |
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